Numpy----------mathematical calculations
Pandas-----------data manipulation
Matplotlib--------data vizualization
Seaborn--------statistical data vizualization

plotly:
it is used for vizualizing the data in a interactive way where we can built:
dashboards
web based vizuzlizations
3d plots
effective charts/graphs

with the help of plotly library we can
Zoom
click
pan
build dashboards for ready applications

we can easily integrate the plotly library with different libraries like numpy and pandas

pip install plotly

import plotly.express as px

plotly consists of different plots like:
line plot
scatter plot
histogram
bar chart
heat map
box plot
pie chart
area plot 
3d vizualization

1.line plot:
line plot is used to show the trends in the dataset
mainly used in:
time series analysis
trends in the dataset
stock prices
growth analysis

import numpy as np
import plotly.express as px

x=np.arange(1,10,0.5)
y=np.sin(x)
fig=px.line(x=x,y=y,title="Sine wave line plot")
fig.show()

C:\Users\pc\New folder\python\Lib\site-packages\kaleido\_sync_server.py:11: UserWarning:


Warning: You have Plotly version 5.24.1, which is not compatible with this version of Kaleido (1.2.0).

This means that static image generation (e.g. `fig.write_image()`) will not work.

Please upgrade Plotly to version 6.1.1 or greater, or downgrade Kaleido to version 0.2.1.

np.sin(1)

0.8414709848078965

2.scatter plot:
it is used to show the relationships between the variables

import numpy as np
import plotly.express as px

x=np.random.randn(100)
y=np.random.randn(100)

fig=px.scatter(x=x,y=y,title="Random scatter plot")
fig.show()

3.Histograms:
it is used for the data distribution analysis

data=np.random.normal(25,5,500)
fig=px.histogram(data,title="Normal data distribution analysis")
fig.show()

4.Bar chart:
it is used for comparing the categorical data
sales by region
count analysis

Transport=["Bus","Car","Bike","Auto"]
values=np.random.randint(50,100,4)

fig=px.bar(x=Transport,y=values,title="Count of transportation")
fig.show()

5.Heat map:
it is used for the correlation analysis
where it shows the matrix form

data=np.random.rand(10,10)
fig=px.imshow(data,title="Heatmap analysis")
fig.show()

6.Box plot:
it is used for the outlier detection

data=np.random.normal(100,20,200)
fig=px.box(data,title="Box plot Analysis")
fig.show()

7.pie chart
it is used to show the percentage distribution

labels=["Laptops","Mobiles","Tablet"]
values=np.random.randint(10,50,3)
fig=px.pie(names=labels,values=values,title="Share on products")
fig.show()

8.Area plot:
used for cummulative analysis
used to show the time series growth analysis

x=np.arange(0,10)
y=np.random.randint(1,20,10)
px.area(x=x,y=y,title="Area plot")

plotly mainly consists of two interfaces:

1.plotly.express(px)

very simple
one-line chart


2.graph objects(go)

we can do more customization
mainly used in dashboards

import plotly.graph_objects as go

x=np.arange(5)
y=np.random.randint(1,10,5)

go.Figure(data=go.Bar(x=x,y=y))

complete sales dashboard using plotly

monthly sales trend----line plot
sales by region----bar
correlation analysis---heatmap
profit distribution---histograms


Numpy(data generation)
plotly(vizualization)
subplots(building dashboard style)

import numpy as np
import pandas as pd
np.random.seed(42)

n=500

data=pd.DataFrame({
    "Month":np.random.choice(
        ["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],n),
    "Region":np.random.choice(
        ["North","South","East","West"],n),
    "Category":np.random.choice(
        ["Electronics","Clothing","Furniture"],n),
    "Sales":np.random.randint(1000,10000,n),
    "Profit":np.random.randint(200,3000,n),
    "Quantity":np.random.randint(1,10,n)
})

data

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.express as px

monthly_sales=data.groupby("Month")["Sales"].sum().reindex(
    ["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"])
region_sales=data.groupby("Region")["Sales"].sum()
category_sales=data.groupby("Category")["Sales"].sum()
corr=data[['Sales','Profit','Quantity']].corr()

fig=make_subplots(
    rows=3,cols=2,
    specs=[[{"colspan":2},None],
           [{"type":"bar"},{"type":"pie"}],
           [{"type":"histogram"},{"type":"heatmap"}]],
    subplot_titles=(
        "Monthly sales trend",
        "sales by region",
        "sales by category",
        "profit distribution",
        "correlation heatmap"
    ))
fig.add_trace(
    go.Scatter(x=monthly_sales.index,y=monthly_sales.values,
               mode="lines+markers",name="Monthly Sales"),
    row=1,col=1)

fig.add_trace(
    go.Bar(x=region_sales.index,y=region_sales.values,
               name="Region Sales"),
    row=2,col=1)

fig.add_trace(
    go.Pie(labels=category_sales.index,values=category_sales.values),
    row=2,col=2)

fig.add_trace(
    go.Histogram(x=data['Profit'],nbinsx=30),
    row=3,col=1)

fig.add_trace(
    go.Heatmap(
        z=corr.values,
        x=corr.columns,
        y=corr.columns),row=3,col=2)
fig.update_layout(height=800,width=700,title_text="COMPLETE SALES ANALYTICS DASHBOARD",showlegend=False)
fig.show()

mini projects:
stock price prediction
sales analysis dashboard

import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots

np.random.seed(42)

# -----------------------------
# Generate Synthetic Stock Data
# -----------------------------
days = 200
dates = np.arange(days)

price = np.cumsum(np.random.normal(0, 1, days)) + 100
open_price = price + np.random.normal(0, 1, days)
close_price = price + np.random.normal(0, 1, days)
high_price = np.maximum(open_price, close_price) + np.random.rand(days) * 2
low_price = np.minimum(open_price, close_price) - np.random.rand(days) * 2
volume = np.random.randint(1000, 5000, days)

# -----------------------------
# Moving Averages
# -----------------------------
ma20 = np.convolve(close_price, np.ones(20)/20, mode='same')
ma50 = np.convolve(close_price, np.ones(50)/50, mode='same')

# -----------------------------
# RSI Calculation
# -----------------------------
delta = np.diff(close_price, prepend=close_price[0])
gain = np.where(delta > 0, delta, 0)
loss = np.where(delta < 0, -delta, 0)

avg_gain = np.convolve(gain, np.ones(14)/14, mode='same')
avg_loss = np.convolve(loss, np.ones(14)/14, mode='same')

rs = avg_gain / (avg_loss + 1e-10)
rsi = 100 - (100 / (1 + rs))

# -----------------------------
# MACD Calculation
# -----------------------------
ema12 = np.convolve(close_price, np.ones(12)/12, mode='same')
ema26 = np.convolve(close_price, np.ones(26)/26, mode='same')
macd = ema12 - ema26
signal = np.convolve(macd, np.ones(9)/9, mode='same')

# -----------------------------
# Create Dashboard Layout
# -----------------------------
fig = make_subplots(
    rows=4, cols=1,
    shared_xaxes=True,
    vertical_spacing=0.02,
    row_heights=[0.5, 0.15, 0.15, 0.2],
    subplot_titles=("Stock Price", "Volume", "RSI", "MACD")
)

# -----------------------------
# Candlestick
# -----------------------------
fig.add_trace(
    go.Candlestick(
        x=dates,
        open=open_price,
        high=high_price,
        low=low_price,
        close=close_price,
        name="Candlestick"
    ),
    row=1, col=1
)

# Moving Averages
fig.add_trace(go.Scatter(x=dates, y=ma20, mode='lines', name='MA20'), row=1, col=1)
fig.add_trace(go.Scatter(x=dates, y=ma50, mode='lines', name='MA50'), row=1, col=1)

# -----------------------------
# Volume
# -----------------------------
fig.add_trace(
    go.Bar(x=dates, y=volume, name="Volume"),
    row=2, col=1
)

# -----------------------------
# RSI
# -----------------------------
fig.add_trace(
    go.Scatter(x=dates, y=rsi, mode='lines', name="RSI"),
    row=3, col=1
)

# RSI Overbought/Oversold Lines
fig.add_hline(y=70, line_dash="dash", row=3, col=1)
fig.add_hline(y=30, line_dash="dash", row=3, col=1)

# -----------------------------
# MACD
# -----------------------------
fig.add_trace(go.Scatter(x=dates, y=macd, mode='lines', name="MACD"), row=4, col=1)
fig.add_trace(go.Scatter(x=dates, y=signal, mode='lines', name="Signal"), row=4, col=1)

# -----------------------------
# Layout Settings
# -----------------------------
fig.update_layout(
    height=1000,
    title="📈 Professional Stock Market Analytics Dashboard",
    template="plotly_dark",
    xaxis_rangeslider_visible=False
)

fig.show()

import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots

np.random.seed(42)

# -----------------------------
# Synthetic Business Data
# -----------------------------
days = np.arange(1, 101)
sales = np.cumsum(np.random.randint(50, 150, 100))
profit = sales - np.random.randint(1000, 3000, 100)
customers = np.random.randint(20, 100, 100)

# -----------------------------
# Create Subplots Layout
# -----------------------------
fig = make_subplots(
    rows=3, cols=2,
    specs=[
        [{"colspan": 2}, None],
        [{"type": "scatter"}, {"type": "bar"}],
        [{"type": "pie"}, {"type": "indicator"}]
    ],
    subplot_titles=(
        "Sales & Profit Trend",
        "Customer Growth",
        "Daily Customers",
        "Revenue Distribution",
        "KPI Performance"
    )
)

# -----------------------------
# Main Trend Line (Sales)
# -----------------------------
fig.add_trace(
    go.Scatter(
        x=days,
        y=sales,
        mode="lines",
        name="Sales"
    ),
    row=1, col=1
)

# Profit Line
fig.add_trace(
    go.Scatter(
        x=days,
        y=profit,
        mode="lines",
        name="Profit"
    ),
    row=1, col=1
)

# -----------------------------
# Customer Scatter Plot
# -----------------------------
fig.add_trace(
    go.Scatter(
        x=days,
        y=customers,
        mode="markers",
        name="Customers"
    ),
    row=2, col=1
)

# -----------------------------
# Bar Chart (Monthly Aggregation)
# -----------------------------
monthly_sales = np.array_split(sales, 5)
monthly_sum = [m.sum() for m in monthly_sales]

fig.add_trace(
    go.Bar(
        x=["M1","M2","M3","M4","M5"],
        y=monthly_sum,
        name="Monthly Sales"
    ),
    row=2, col=2
)

# -----------------------------
# Pie Chart
# -----------------------------
fig.add_trace(
    go.Pie(
        labels=["Product A","Product B","Product C"],
        values=[40, 35, 25],
        hole=0.4
    ),
    row=3, col=1
)

# -----------------------------
# KPI Gauge
# -----------------------------
fig.add_trace(
    go.Indicator(
        mode="gauge+number+delta",
        value=np.mean(profit),
        delta={'reference': 5000},
        gauge={
            'axis': {'range': [0, 15000]},
        },
        title={'text': "Avg Profit"}
    ),
    row=3, col=2
)

# -----------------------------
# Dropdown Filter
# -----------------------------
fig.update_layout(
    updatemenus=[
        dict(
            buttons=[
                dict(label="Show All",
                     method="update",
                     args=[{"visible": [True]*6}]),
                dict(label="Only Sales",
                     method="update",
                     args=[{"visible": [True, False, False, False, False, False]}]),
                dict(label="Only Customers",
                     method="update",
                     args=[{"visible": [False, False, True, False, False, False]}])
            ],
            direction="down",
            showactive=True,
            x=0.1,
            y=1.15
        )
    ]
)

# -----------------------------
# Range Slider
# -----------------------------
fig.update_xaxes(
    rangeslider_visible=True,
    row=1, col=1
)

# -----------------------------
# Layout Settings
# -----------------------------
fig.update_layout(
    height=850,
    width=850,
    title="🚀 Industry-Level Interactive Business Dashboard",
    template="plotly_dark"
)

fig.show()

what is plotly?
what are the different interfaces available in the plotly?
difference between plotly express and graph objects?
how do we create subplots in plotly?
how do you export plotly charts?
how to make interactive dashboards?
what is dashboard?
how to create a dashboard?
what is the use of heatmap?
what are the different types of dashboards available?

projects:
stock price prediction
sales analysis dashboard
uber data dashboard
blinklit related data dashboard

	Month	Region	Category	Sales	Profit	Quantity
0	Jul	South	Furniture	2409	2051	5
1	Apr	South	Furniture	5566	580	6
2	Nov	South	Furniture	5491	1846	6
3	Aug	West	Electronics	2555	213	5
4	May	North	Furniture	1804	703	7
...	...	...	...	...	...	...
495	Jun	North	Furniture	1001	2845	1
496	Sep	South	Electronics	1374	709	8
497	Dec	East	Furniture	5648	1406	7
498	May	East	Clothing	3365	585	7
499	Jan	West	Furniture	2525	1797	3